Ubiquitous Traffic Management with Fuzzy Logic - Case Study of Maseru, Lesotho

Conference ProceedingsMaseru is the capital city of Lesotho and is a relatively small city with roughly 67 vehicles registered each day. Traffic lights are used with the intension of effectively managing vehicular traffic at junctions. These traffic lights follow a predetermined sequence usually based on historic data. As a result of this design, they inherently fail to efficaciously manage traffic flow when it is abnormal. Vehicles on one side have to wait even though there are no cars on other sides of the road. The consequences of this include increased congestion and atmospheric air pollution. Technological advancements have resulted in the now widely researched Internet of Things paradigm with one of its applications being vehicular traffic management. The focus of this paper is the design of a prototype reactive system based on Internet of Things whose functionality includes traffic lights that are capable of reacting to prevailing conditions. The system makes use of Radio Frequency IDentifier technology and mobile tools to ubiquitously collect traffic data and disseminate value added traffic information

A Security Algorithm for Wireless Sensor Networks in the Internet of Things Paradigm

Conference ProceedingsIn this paper we explore the possibilities of having an algorithm that can protect Zigbee wireless sensor networks from intrusion; this is done from the Internet of Things paradigm. This algorithm is then realised as part of an intrusion detection system for Zigbee sensors used in wireless networks. The paper describes the algorithm used, the programming process, and the architecture of the system developed as well as the results achieved

Development of Adaptive Environmental Management System: A Participatory Approach through Fuzzy Cognitive Maps

Conference ProceedingsMining industries develop environmental management systems/plans to mitigate the impact their operations has on the society. Even with these plans, there are still issues of pollution affecting the society. Though there are ICT-based pollution monitoring solutions, their use is dismal due to lack of appreciation or understanding of the disseminated information. This result in mining communities depending on their own local knowledge to observe, monitor and predict miningrelated environmental pollution. However, this local knowledge has never been tested scientifically or analysed to recognize its usability or effectiveness. Mining companies tend to ignore this knowledge from the communities as it is treated like common information with no much scientific value. As a step towards verifying or validating this local knowledge, we demonstrate how fuzzy cognitive maps can be used to model, analyse and represent this linguistic local knowledge

Cloud SAMS: Cloud computing solution for public schools within South Africa's ‘second economy’

Published Conference ProceedingsCloud computing is coming of age; it involves on-demand access to a shared pool of configurable computing resources. There is an emerging consensus that cloud computing will play a critical role in redressing the digital divide especially in rural areas of Africa. In this paper, we report on a success story to this end; the use of cloud computing in expanding the access of students' records management system to resource-constrained schools in the Free State province of South Africa. This was motivated by the fact that despite the proven tight correlation between availability of data and quality of education, many schools that are considered part of the `second economy' in South Africa continue to operate in uni-direction data flow arrangements that do not provide them with adequate data for critical decision making. We implemented and evaluated a Cloud based School Administration and Management System; hereby called `Cloud SAMS' for these resource-constrained schools in the province (they account for over 80% of all schools). Starting off with 5 schools and later ramping it up to 50, `Cloud SAMS' enables schools to securely and privately share one copy of the system maintained in the cloud; this brings on board several benefits - low cost, faster implementation and resilience to failures

A Framework for Accurate Drought Forecasting System Using Semantics-Based Data Integration Middleware

Published Conference ProceedingsTechnological advancement in Wireless Sensor Networks (WSN) has made it become an invaluable component of a reliable environmental monitoring system; they form the ‘digital skin’ through which to ‘sense’ and collect the context of the surroundings and provides information on the process leading to complex events such as drought. However, these environmental properties are measured by various heterogeneous sensors of different modalities in distributed locations making up the WSN, using different abstruse terms and vocabulary in most cases to denote the same observed property, causing data heterogeneity. Adding semantics and understanding the relationships that exist between the observed properties, and augmenting it with local indigenous knowledge is necessary for an accurate drought forecasting system. In this paper, we propose the framework for the semantic representation of sensor data and integration with indigenous knowledge on drought using a middleware for an efficient drought forecasting system

Using fuzzy cognitive maps in modelling and representing weather lore for seasonal weather forecasting over east and Southern Africa

Published ArticleThe creation of scientific weather forecasts is troubled by many technological challenges while their utilization is dismal. Consequently, the majority of small-scale farmers in Africa continue to consult weather lore to reach various cropping decisions. Weather lore is a body of informal folklore associated with the prediction of the weather based on indigenous knowledge and human observation of the environment. As such, it tends to be more holistic and more localized to the farmers’ context. However, weather lore has limitations such as inability to offer forecasts beyond a season. Different types of weather lore exist and utilize almost all available human senses (feel, smell, sight and hear). Out of all the types of weather lore in existence, it is the visual or observed weather lore that is mostly used by indigenous societies to come up with weather predictions. Further, meteorologists continue to treat weather lore knowledge as superstition partly because there is no means to scientifically evaluate and validate it. The visualization and characterization of visual sky objects (such as moon, clouds, stars, rainbow, etc) in forecasting weather is a significant subject of research. In order to realize the integration of visual weather lore knowledge in modern weather forecasting systems, there is a need to represent and scientifically substantiate weather lore. This article is aimed at coming up with a method of organizing the weather lore from the visual perspective of humans. To achieve this objective, we used fuzzy cognitive mapping to model and represent causal relationships between weather lore concepts and weather outcomes. The results demonstrated that FCMs are efficient for matrix representation of selected weather outcome scenarios caused visual weather lore concepts. Based on these results the recommendation of this study is to use this approach as a preliminary processing task towards verifying weather lore

Downscaling Africa’s Drought Forecasts through Integration of Indigenous and Scientific Drought Forecasts Using Fuzzy Cognitive Maps

In the wake of increased drought occurrences being witnessed in Sub-Saharan Africa, more localized and contextualized drought mitigation strategies are on the agendas of many researchers and policy makers in the region. The integration of indigenous knowledge on droughts with seasonal climate forecasts is one such strategy. The main challenge facing this integration, however, is the formal representation of highly-structured and holistic indigenous knowledge. In this paper, we demonstrate how the use of fuzzy cognitive mapping can address this challenge. Indigenous knowledge on droughts from five communities was modeled and represented using fuzzy cognitive maps. Maps from one of these case communities were then used in the implementation of the integration framework, called itiki

ITIKI: Bridge between African indigenous knowledge and modern science on drought prediction

The now more rampant and severe droughts have become synonymous with Sub-Saharan Africa; they are a major contributor to the acute food insecurity in the Region. Though this scenario may be replicated in other regions in the globe, the uniqueness of the problem in Sub-Saharan Africa is to be found in the ineffectiveness of the drought monitoring and predicting tools in use in these countries. Here, resource-challenged National Meteorological Services are tasked with drought monitoring responsibility. The main form of forecasts is the Seasonal Climate Forecasts whose utilisation by small-scale farmers is below par; they instead consult their Indigenous Knowledge Forecasts. This is partly because the earlier are too supply-driven, too ""coarse"" to have meaning at the local level and their dissemination channels are ineffective. Indigenous Knowledge Forecasts are under serious threat from events such as climate variations and ""modernisation""; blending it with the scientific forecasts can mitigate some of this. Conversely, incorporating Indigenous Knowledge Forecasts into the Seasonal Climate Forecasts will improve its relevance (cultural and local) and acceptability, hence boosting its utilisation among small-scale farmers. The advantages of such a mutual symbiosis relationship between these two forecasting systems can be accelerated using ICTs. This is the thrust of this research: a novel drought-monitoring and predicting solution that is designed to work within the unique context of small-scale farmers in Sub-Saharan Africa. The research started off by designing a novel integration framework that creates the much-needed bridge (itiki) between Indigenous Knowledge Forecasts and Seasonal Climate Forecasts. The Framework was then converted into a sustainable, relevant and acceptable Drought Early Warning System prototype that uses mobile phones as input/output devices and wireless sensor-based weather meters to complement the weather stations. This was then deployed in Mbeere and Bunyore regions in Kenya. The complexity of the resulting system was enormous and to ensure that these myriad parts worked together, artificial intelligence technologies were employed: artificial neural networks to develop forecast models with accuracies of 70% to 98% for lead-times of 1 day to 4 years; fuzzy logic to store and manipulate the holistic indigenous knowledge; and intelligent agents for linking the prototype modules

Framework for Predicting Droughts in Developing Countries Using Sensor Networks and Mobile Phones

Drought is the most complex and least understood of all natural disasters and it affects more people than any other hazard. Droughts have become synonymous with the developing countries and in particular the Sub-Saharan Africa where the hazard is chronic. Effects of droughts can be mitigated if accurate and timely drought predications were to be done. Unfortunately, despite the enormous advancements in science, predictions only provide indications of trends. A major weakness of the existing tools is the emphasis on macro/international level information. The tools also tend to ignore the at risk community who happen to be host to very crucial traditional knowledge on droughts. In this paper, we propose an integrated drought predication framework that considers both scientific and traditional knowledge and combines the use of mobile phones with wireless sensor networks to be able to capture and relay micro drought parameters. The framework is an enhancement of ITU’s Ubiquitous Sensor Network (USN) Layers. In order to accommodate the diverse roles mobile phones play in our framework, Layer 2 (USN Access Networking) is implemented using three sub-layers composed of heterogeneous gateways

Adaptive Environmental Management System for Lejweleputswa District: A Participatory Approach Through Fuzzy Cognitive Maps

Conference ProceedingsSouth Africa is home to some of the deepest mines in the world. Waste from gold mines constitutes the largest single source of waste and pollution in South Africa [2] Though mining industries develop environmental management systems/plans to identify and mitigate the impacts their operations has on the society, their outcome still poses a threat in terms of environmental pollution to communities around them. There are many ICT-based pollution monitoring solutions, but they do not address the needs of the affected mining communities. Some of the reasons for this include lack of relevant tools to access the systems (smartphones, computers) as well as lack of understanding and appreciation of the disseminated information. The mining communities around Lejweleputswa (South Africa) have learnt to depend on their own local knowledge to prevent or mitigate the impacts that mining operations has on them